Enhancing the Power of Polyhedral-Based Optimizations with Coordinate-Based Hill Climbing

2026-09-02

AI Summary

本文通过在多面体编译器Pluto中加入基于坐标的爬山调优方法，调整数字转换参数以优化内核性能，解决了传统优化方法难以达到最优解的问题。

Abstract

This paper describes our experience extending the polyhedral compiler Pluto with a lightweight, coordinate-wise hill-climbing tuner that adjusts numeric transformation parameters, such as tile sizes and thread-block dimensions, after Pluto selects the kernel's loop structure. To ensure fast convergence and escape local minima, hill climbing is augmented with two techniques: expanded neighborhood exploration and a shortest-hop refinement phase. On x86 and ARM CPUs, tuned kernels outperform Pluto's default configuration (1.06-1.28x geometric mean speedup across 11 benchmarks) and static optimizers (Clang -O3, Polly, IOOpt), reaching performance competitive with the AutoTVM autotuner at substantially lower search cost. Applying the same technique to GPU thread-block allocation on an NVIDIA A100 yields 5.5-8.5% improvement over default configurations. These results position post-optimization parameter tuning as a practical middle ground between fixed-cost-model polyhedral compilation and full autotuning.

Problem

• polyhedral compiler
• parameter tuning
• performance optimization
• hill climbing

Innovation

• coordinate-based hill climbing
• polyhedral compilation
• parameter tuning
• neighborhood exploration
• shortest-hop refinement